Generative Adversarial Networks. 
Generative Adversarial Network, i.e., GANs [1]} has attracted intense attention and has been extensively studied for a long time.
For example, DCGAN [2]} greatly enhanced the capability of GANs by introducing the convolutional layer.
The recent works [3]}, [4]}, [5]}, [6]} employed a series of advanced network architecture to further improve the fidelity of the generated images.
In addition, several novel loss functions  [7]}, [3]}, [9]}, [10]} were designed to stabilize the adversarial training stage of GANs.
A popular research direction of GANs is image-to-image translation.
The image-to-image translation is designed to transfer images from the source domain to the target domain while retaining the content representation of the image.
Image-to-image translation is widely used in computer vision and image processing, such as semantic image synthesis [11]}, [12]}, style transfer [13]}, [14]}, super resolution [15]}, [16]}, image painting [17]}, [18]} and so on.
